Stochastic approaches to mobility prediction, path planning and motion control for ground vehicles in uncertain environments
Name
613216740-MIT.pdf
Description
Full printable version
Size
11.4 MB
Format
Adobe PDF
Checksum (MD5)
a89edc7dd605721d3a5b16de785147af
Author(s)
Kewlani, Gaurav
Advisor(s)
Karl lagnemma.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
The ability of autonomous or semi-autonomous unmanned ground vehicles (UGVs) to rapidly and accurately predict terrain negotiability, generate efficient paths online and have effective motion control is a critical requirement for their safety and use in unstructured environments. Most techniques and algorithms for performing these functions, however, assume precise knowledge of vehicle and/or environmental (i.e. terrain) properties. In practical applications, significant uncertainties are associated with the estimation of the vehicle and/or terrain parameters, and these uncertainties must be considered while performing the above tasks. Here, computationally inexpensive methods based on the polynomial chaos approach are studied that consider imprecise knowledge of vehicle and/or terrain parameters while analyzing UGV dynamics and mobility, evaluating safe, traceable paths to be followed and controlling the vehicle motion. Conventional Monte Carlo methods, that are relatively more computationally expensive, are also briefly studied and used as a reference for evaluating the computational efficiency and accuracy of results from the polynomial chaos-based techniques.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 107-111).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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